In many fields, users must access and modify data located elsewhere on their own computers or workstations. In some situations, the volume of data needed by a user can be large and therefore it can be time consuming to download the needed information on demand. One example of such a situation is in the medical field where information about patients is stored in a central database. A patient typically undergoes various tests and examination procedures, such as x-ray, MRI, etc., and the results of these various procedures are stored in a common or distributed database. Medical personnel then access this information (“patient studies”) for diagnostic and evaluative purposes.
Strategies have been devised by which users requiring such information can pre-fetch the patient studies prior to use so that the information is immediately available to them when needed.
Existing pre-fetching strategies currently follow fixed rules. For example, a typical rule is to pre-fetch all unread studies from the current day over the night so that by morning of the next day, all the studies reside at the users' workstation. The users do not have to load the studies from the previous day, because they were already pre-fetched at their workstation resulting in a loading time advantage.
This has been implemented in a number of ways. For example, as taught by Rogan in Rogan's “Everything On-line” technology makes pre-fetching of digital medical images superfluous, http://www.hoise.cornlvmw/00/articles/vmw/LV-VM-04-00-27 html, Jun. 27, 2005 (herein incorporated by reference), a medical specialist presents a list of images which he wants to consult a day on beforehand in order to timely load them into the system. Overnight, the required images are then fetched in batch-mode out of a Picture Archiving and Communication System (PACS) to place them in stand-by locally. Proceeding in this manner was necessary since the available bandwidth often does not allow to directly download very large images. In most cases, local workstations have insufficient disk capacity to load a large number of large-sized image files.
However, the strategy proposed by Rogan is a static pre-fetching strategy, which does not take into account dynamic changes and the individual user behavior, as well as different individual hospital environments. Also this brute-force approach does not allow for fine tuned adjustments and/or for transferring only the relevant images in contrast to transferring the whole study.
Another approach is provided by BRIT Systems' Roentgen Files (see BRIT Systems Roentgen Files, http://www.brit.com/RoentgenFiles.htm, Jun. 27, 2005, herein incorporated by reference). In this system, pre-fetching is based on body-region, modality type and exam date. These downloaded exams can be pre-loaded to the viewer according to table driven rules. As with Rogan, pre-fetching is table driven or, in other words, static and not self-adapting and dynamic.
What is needed is an adaptive system that dynamically updates the pre-fetching strategy based on, e.g., an individual hospital environment and user behavior. Such an adaptive/dynamic system could result in a higher optimization potential than merely using static rules.